A Depth-Guided Local Outlier Rejection Methodology for Robust Feature Matching in Urban UAV Images

초록

Highlights What are the main findings? The proposed depth-guided local outlier rejection methodology integrates monocular depth estimation, DBSCAN clustering, and localized model estimation to improve feature matching reliability in complex urban UAV imagery. Higher Recall and F1-score were achieved than with conventional 2D-based outlier rejection methods, while comparable Precision was maintained, demonstrating robust inlier preservation under depth and viewpoint variations. What are the implications of the main findings? Incorporating single-image depth information enhances geometric consistency and registration stability in depth-varying urban environments. The methodology effectively corrects depth- and viewpoint-related mismatches, enhancing UAV image registration reliability.Highlights What are the main findings? The proposed depth-guided local outlier rejection methodology integrates monocular depth estimation, DBSCAN clustering, and localized model estimation to improve feature matching reliability in complex urban UAV imagery. Higher Recall and F1-score were achieved than with conventional 2D-based outlier rejection methods, while comparable Precision was maintained, demonstrating robust inlier preservation under depth and viewpoint variations. What are the implications of the main findings? Incorporating single-image depth information enhances geometric consistency and registration stability in depth-varying urban environments. The methodology effectively corrects depth- and viewpoint-related mismatches, enhancing UAV image registration reliability.Abstract Urban UAV imagery presents challenges for reliable feature matching owing to complex 3D structures and depth discontinuities. Conventional 2D-based outlier rejection methods often fail to maintain geometric consistency under significant altitude variations or viewpoint differences, resulting in the rejection of valid correspondences. To overcome these limitations, a depth-guided local outlier rejection methodology is proposed which integrates monocular depth estimation, DBSCAN-based clustering, and local geometric model estimation. Depth information estimated from single UAV images is combined with feature correspondences to form pseudo-3D coordinates, enabling spatially localized registration. The proposed method was quantitatively evaluated in terms of Precision, Recall, F1-score, and Number of Matches, and was applied as a depth-guided front-end to three representative 2D-based outlier rejection schemes (RANSAC, LMedS, and MAGSAC++). Across all image sets, the depth-guided variants consistently achieved higher Recall and F1-score than their conventional 2D counterparts, while maintaining comparable Precision and keeping mismatches low. These results indicate that introducing depth-guided pseudo-3D constraints into the outlier rejection stage enhances geometric stability and correspondence reliability in complex urban UAV imagery. Accordingly, the proposed methodology provides a practical and scalable solution for accurate registration in depth-varying urban environments.

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